Much obliged. 👍

Gotcha'. Isn't PeerID just a hash of a public key? It's pretty trivial to generate a bunch of those and self-confirm, no?

How does one prevent self-confirmation? If it's anonymous, can I confirm myself a dozen times?

You might be on to something. Not necessarily the inf norm, but maybe an asymmetric loss function. Guess zero when it's 0.1 and the penalty is much higher than guessing 0.1 when it is 0.

Wouldn't an auto encoder run into the same issue? If the dataset is mostly zeros then every loss function I can think of would hit the same issue. PCA could be an option, but disappointing to introduct it into what is otherwise a pure UNet architecture.

Is there a good sparse loss function that also does regression? I have what basically amounts to an image to image problem, but the resulting image is a dense UV set (red channel goes from 0-255, green from 0-255). Most of the image is "no signal" so MSE tends to just predict all zeros after a while. I can't split the image into multuple channels because softmax over 255 values for red and 255 more channels for green would make me OOM. I might try and narrow it down to just 16 quantized channels each, but I'd really rather spit out a two channel image and do clever losses on that. I'm sure masking has some clever tricks like union over intersection, but those don't seem to handle regression cases, only boolean.